64 research outputs found
FACTORS PRECEIVED TO INFLUENCE THE PERFORMANCE OF PUBLIC SECTOR EMPLOYEES WITH MEDIATING EFFECT OF EMPLOYEES SATISFACTION; A CASE STUDY OF CIVIL DEFENCE DIRECTORATE
No full textIn developing countries like Pakistan, mostly the public sector departments give not as much of focus on the performance / capacity building as well as the satisfaction of the employees, the research was conducted to know the influence of the factors perceived on the performance of public sector employees by increasing satisfaction and for this purpose the Civil Defence Department was selected. The study was conducted on the mixed method approach in which both qualitative and quantitative methodologies were adopted to know the responses of the employees of the Civil Defence Department regarding research objectives. Questionnaires, both in open ended and close ended format were distributed among 100 employees of the Civil Defence Department as per the availability of the skeleton staff during COVID-19. The hypotheses were equipped to recognize the significance relationship of variables as well as statistical analysis was applied through SPSS to examine the acceptance or rejection of the hypothesis. Hence, the result instigated that factors perceived in this study have significance influenced on the performance and satisfaction of the public sector employees
Antibody Response of COVID-19 Vaccine in Subjects after Renal Transplantation - A Case Control Study
No full textObjective: To evaluate the immune response to COVID-19 vaccination in patients after renal transplantation.
Study Design: A case-control study.
Place and Duration of Study: The study was conducted at the Kidney Transplant Department, Shaikh Zayed
Hospital Lahore, Pakistan from September 2020 to September 2021.
Methods: A total of 180 renal transplant recipients who had received a third dose of the Pfizer vaccine were
selected for the study. The patients were evaluated before and after vaccine administration for seropositivity
and anti-spike antibody levels. The results were compared with a control group of 50 healthy controls. T-cell
response was assessed in only 50 transplant recipients.
Results: 50 (27.7%) KTRs and 48 (96%) controls achieved optimum antibody level (4160 AU/ml) (P<.001). All
(100%) of the controls were seropositive after 3rd dose. Among kidney transplant recipients (KTRs), there was a
significant increase in the median anti-spike antibody level from 13.7 AU/ml before to 515.46 AU/ml after the
3rd dose (P< .001). There also was a significant increase in log-transformed antibody level after 3rd dose in both
the study and control group (P<.001). Of 50 randomly selected subjects, 6 (12%) showed positive T cell
response.
Conclusion: Pfizer vaccine improved antibody response in renal transplantation recipients.
How to cite this: Afzal U, Hussain M, Khan AI, Adnan M, Muzammil M, Ahmad I. Antibody Response of COVID-19 Vaccine in Subjects After Renal Transplantation - A Case Control Study. Life and Science. 2024; 5(4): 465-470. doi: http://doi.org/10.37185/LnS.1.1.50
Energy management of smart homes over fog-based IoT architecture
No full textExisting research studies on home automation systems mostly conserve energy by modeling the occupancy of users within home. Some others apply statistical approaches on the survey data about usage of appliances. Consequently, these research works either reduce wastage of electricity through automation or achieve energy efficiency based on appliances’ usage estimations. However, they do not provide energy consumption modeling which is human comfort centric and also validated through practical implementation in real-world smart homes. We present a Markov-chain-based probabilistic model to obtain users stochastic activity patterns which are used to forecast the energy consumption in a smart home environment. These predictions are then leveraged by our novel comfort aware energy saving mechanism named as prediction- and feedback-based proactive energy conservation (PF-PEC) algorithm. The PF-PEC algorithm reduces the total energy consumption while ensuring standard human comfort. Furthermore, a fog-based Internet of Things (IoT) architecture is implemented and deployed in a smart home to efficiently incorporate the proposed algorithm in real-world scenarios. Experimental results show up to 36% energy conservation, marking substantial reduction in daily electricity usage.</p
Formulation and Optimization of Aceclofenac Loaded Nanosuspension : Umair Afzal, Syed. Muhammad Aun, Muhammad Khuram Shahzad, Razi Ullah Khan, Syed Ahmad Shah, Alishba Ch
No full textBackground: The aceclofenac sodium is used as non-steroidal anti inflammatory drug (NSAID), administered through the oral route, causing numerous side effects like nausea, vomiting, dyspepsia, gastrointestinal bleeding and peptic ulcer. NSAIDs, promote the secretions of hydrochloric acid which cause severe damage to the walls of gastro intestinal tracts well as to the drug.
Objectives: Aceclofenac was added in combination with different polymers to form a uniform nano suspension.
Methodology: Three sets of formulations were prepared by using silverson mixer. The organoleptic evaluation, particle size determination, assay, in vitro drug release study and drug excipient compatibility studies were performed. The results were obtained on the basis of drug and excipients compatibility studies, scanning electron microscopy, In vitro drug release study and assay studies.
Results: In this study, the nanosuspension of aceclofenac sodium is produced, which spread on the wider surface area of GIT. Due to this reason, the decrease in accumulated concentration, the secretion of hydrochloric acid is not promoted, the chance of damage to GIT is drastically reduced, and the drug also escaped from the damage and the absorption of drug is also increased.
Conclusion: The F1 formulation showed best results as compared to other two formulations. These results were obtained due to the effect of polymers used in combination with drug and surfactant
Formulation and Optimization of Aceclofenac Loaded Nanosuspension : Umair Afzal, Syed. Muhammad Aun, Muhammad khuram shahzad, Razi ullah khan, Syed Ahmad Shah, Alishba Ch
No full textBackground: The aceclofenac sodium is used as non-steroidal anti inflammatory drug (NSAID), administered through the oral route, causing numerous side effects like nausea, vomiting, dyspepsia, gastrointestinal bleeding and peptic ulcer. NSAIDs, promote the secretions of hydrochloric acid which cause severe damage to the walls of gastro intestinal tracts well as to the drug.
Objectives: Aceclofenac was added in combination with different polymers to form a uniform nano suspension.
Methodology: Three sets of formulations were prepared by using silverson mixer. The organoleptic evaluation, particle size determination, assay, in vitro drug release study and drug excipient compatibility studies were performed. The results were obtained on the basis of drug and excipients compatibility studies, scanning electron microscopy, In vitro drug release study and assay studies.
Results: In this study, the nanosuspension of aceclofenac sodium is produced, which spread on the wider surface area of GIT. Due to this reason, the decrease in accumulated concentration, the secretion of hydrochloric acid is not promoted, the chance of damage to GIT is drastically reduced, and the drug also escaped from the damage and the absorption of drug is also increased.
Conclusion: The F1 formulation showed best results as compared to other two formulations. These results were obtained due to the effect of polymers used in combination with drug and surfactant
Clustering-Based Energy Management of Residential Loads by using Artificial Intelligence
No full textDeveloping countries have witnessed a remarkable surge in the energy crisis due to the supply and demand gap. One of the solutions to overcome this problem is the optimal use of energy that can be achieved by employing demand side management (DSM) and demand response (DR) methods intelligently. Machine learning and data analysis tools help us create intelligent systems that motivate us to use machine learning to implement DSM/DR programs. In this paper, a novel DSM algorithm is introduced to implement DSM intelligently by using artificial intelligence. The results show an efficient implementation of an artificial neural network (ANN) along with demand side management, whereas the peak and off-peak loads were normalized to a certain range where a perfect agreement between supply and demand can be reached
Enabling IoT platforms for social IoT applications: Vision, feature mapping, and challenges
No full textOptimising window size of semantic of classification model for identification of in-text citations based on context and intent.
No full textCitations in scientific literature act as channels for the sharing, transfer, and development of scientific knowledge. However, not all citations hold the same significance. Numerous taxonomies and machine learning models have been developed to analyze citations, but they often overlook the internal context of these citations. Moreover, it is worth noting that selecting the appropriate word embedding and classification models is crucial for achieving superior results. Word embeddings offer n-dimensional distributed representations of text, striving to capture the nuanced meanings of words. Deep learning-based word embedding techniques have garnered significant attention and found application in various Natural Language Processing (NLP) tasks, including text classification, sentiment analysis, and citation analysis. Current state-of-the-art techniques often use small datasets with fixed window sizes, resulting in the loss of contextual meaning. This study leverages two benchmark datasets encompassing a substantial volume of in-text citations to guide the selection of an optimal word embedding window size and classification approaches. A comparative analysis of various window sizes for in-text citations is conducted to identify crucial citations effectively. Additionally, Word2Vec embedding is employed in conjunction with deep learning models and machine learning models such as Convolutional Neural Networks (CNNs), Gated Recurrent Units (GRUs), Long Short-Term Memory (LSTM) networks, Support Vector Machines (SVM), Decision Trees, and Naive Bayes.The evaluation employs precision, recall, F1-score, and accuracy metrics for each combination of window sizes. The findings reveal that, particularly for lengthy in-text citations, larger citation windows are more adept at capturing the semantic essence of the references. Within the scope of this study, window sizes of 10 achieve superior accuracy and precision with both machine and deep learning models
Impact of Green Human Resource Management on Environmental Performance: the Mediating Role of Green Innovation and Environmental Strategy in Pakistan
No full textWith globally increasing recognition of environmental sustainability, now businesses are also acknowledging the requirement of incorporating green practices into their company operation. The study aims in exploring the relation in between the concepts of some sustainability elements such as “green human resource management practices” (GHRMP) and variables like “environmental performances”, with the examination of environmental strategy and green innovation playing as a mediator. The study of GHRMP with respect to its elements such as green recruitment refers to selecting and hiring an employee who has significant knowledge about environmental sustainability. It also consists of an element named green training that refers to teaching and developing a set of skills in employees to take action while protecting the environment within the organization The author specifically examines the connection between them by using the resource-based view theory. They also tested their relationship using the manufacturing firm's ability, motivation, and opportunity (AMO) theory. A survey questionnaire research strategy was used in this study along with a simple random sampling of 247 managers from large manufacturing firms in Punjab of Pakistan. For data analysis, this research used the p-test based on PLS-SEM. The findings showed that the study elements have a direct and major influence on each other especially the GHRMP and strategic environmental approach complement each other in the presence of the sustainable innovative product by the organization. Additionally, environmental strategy (ES) also partially mediated the influence of sustainable innovative products can be termed green innovation (GI), and it has an impact on environmental performance. The result revealed significant suggestions for legislators and manufacturing industries, to promote environmentally responsible actions, manufacturing goods, plus some production methods through a successful incentive program to increase loyalty. To apply the analysis and apply the study variables in the selected large sample sizes that can also be retested for future research, further research can exclude the novelty and unique issue by replicating the same research in other regions of the world
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